斯坦福大学Shaul Druckmann小组的一项最新研究发现，全脑分析揭示了跨脑区和脑区内部的运动编码结构。相关论文于2025年11月18日发表在《自然—神经科学》杂志上。

在这里，该课题组分析了执行决策任务的小鼠的5万多个神经元的全脑运动相关活动记录。研究人员采用了多种机器学习方法来预测视频中的神经活动，并发现运动相关信号在不同区域之间存在差异，靠近运动外围和运动相关子区域的运动信号更强。描绘预测或跟踪运动的活动揭示了大脑区域内外感觉和运动编码的精细结构。通过单次试验基于视频的行为预测，研究组确定了非指示运动的活动调节及其对选择相关活动分析的影响。他们的工作提供了一幅横跨大脑的运动编码图，以及将神经活动、无指示运动和决策联系起来的方法。

研究人员表示，与运动相关的活动已经在大脑的大部分区域被检测到，包括感觉和运动区域。然而，关于运动相关活动在大脑区域的分布，以及这些活动与神经计算的关系，仍有许多未知之处。

附：英文原文

Title: Brain-wide analysis reveals movement encoding structured across and within brain areas

Author: Wang, Ziyue Aiden, Kurgyis, Balint, Chen, Susu, Kang, Byungwoo, Chen, Feng, Liu, Yi, Liu, Dave, Svoboda, Karel, Li, Nuo, Druckmann, Shaul

Issue&Volume: 2025-11-18

Abstract: Movement-related activity has been detected across much of the brain, including sensory and motor regions. However, much remains unknown regarding the distribution of movement-related activity across brain regions, and how this activity relates to neural computation. Here we analyzed movement-related activity in brain-wide recordings of more than 50,000 neurons in mice performing a decision-making task. We used multiple machine learning methods to predict neural activity from videography and found that movement-related signals differed across areas, with stronger movement signals close to the motor periphery and in motor-associated subregions. Delineating activity that predicts or follows movement revealed fine-scale structure of sensory and motor encoding across and within brain areas. Through single-trial video-based predictions of behavior, we identified activity modulation by uninstructed movements and their impact on choice-related activity analysis. Our work provides a map of movement encoding across the brain and approaches for linking neural activity, uninstructed movements and decision-making.

DOI: 10.1038/s41593-025-02114-x

Source: https://www.nature.com/articles/s41593-025-02114-x